An Adaptive Self-Organizing Migration Algorithm for Parameter Optimization of Wavelet Transformation

Wavelet transformation is well applied in the field of image processing, and parameter optimization of wavelet transformation has always been an eternal topic on its performance improvement. In this paper, an adaptive self-organizing migration algorithm (ASOMA) is proposed to optimize the wavelet pa...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Mathematical problems in engineering Ročník 2022; s. 1 - 14
Hlavní autoři: Cao, Zijian, Jia, Haowen, Zhao, Tao, Fu, Yanfang, Wang, Zhenyu
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York Hindawi 27.02.2022
John Wiley & Sons, Inc
Témata:
Adaptive algorithms
Engineering
Genetic algorithms
Image processing
Methods
Noise
Noise reduction
Optimization
Optimization techniques
Parameters
Performance evaluation
Signal to noise ratio
Wavelet transforms
ISSN:1024-123X, 1563-5147
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:Wavelet transformation is well applied in the field of image processing, and parameter optimization of wavelet transformation has always been an eternal topic on its performance improvement. In this paper, an adaptive self-organizing migration algorithm (ASOMA) is proposed to optimize the wavelet parameters to elevate the performance of wavelet denoising. Firstly, based on the original SOMA, an adaptive step size adjustment method is proposed by recording the step information of successful individuals, which improves the search ability of the SOMA. Secondly, an exploratory selection method of leader is proposed to effectively balance the exploration and exploitation of the SOMA. Finally, ASOMA is compared with the original SOMA and its variants using wavelet general threshold denoising on classical test images in denoising performance, which is evaluated by the indicators of peak signal-to-noise ratio (PSNR) and structural similarity (SSIM). The experimental results demonstrate that ASOMA has better denoising performance than the wavelet general threshold, the original SOMA, and the related variants of SOMA.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1024-123X
1563-5147
DOI:10.1155/2022/6289215